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Observational seasonal influenza relative vaccine effectiveness (rVE) studies employ a variety of statistical methods to account for confounding and biases. To better understand the range of methods employed and implications for policy, we conducted a brief literature review. Across 37 included rVE studies, 10 different types of statistical methods were identified, and only eight studies reported methods to detect residual confounding, highlighting the heterogeneous state of the literature. To improve the comparability and credibility of future rVE research, researchers should clearly explain methods and design choices and implement methods to detect and quantify residual confounding. [ABSTRACT FROM AUTHOR]

Epidemiologic associations estimated from observational data are often confounded by genetics due to pervasive pleiotropy among complex traits. Many studies either neglect genetic confounding altogether or rely on adjusting for polygenic scores (PGS) in regression analysis. In this study, we unveil that the commonly employed PGS approach is inadequate for removing genetic confounding due to measurement error and model misspecification. To tackle this challenge, we introduce PENGUIN, a principled framework for polygenic genetic confounding control based on variance component estimation. In addition, we present extensions of this approach that can estimate genetically unconfounded associations using GWAS summary statistics alone as input and between multiple generations of study samples. Through simulations, we demonstrate superior statistical properties of PENGUIN compared to the existing approaches. Applying our method to multiple population cohorts, we reveal and remove substantial genetic confounding in the associations of educational attainment with various complex traits and between parental and offspring education. Our results show that PENGUIN is an effective solution for genetic confounding control in observational data analysis with broad applications in future epidemiologic association studies., Competing Interests: Competing interests statement:The authors declare no competing interest.

Measurement error and information bias are ubiquitous in epidemiology, yet directed acyclic graphs (DAGs) are infrequently used to represent them, in contrast with confounding and selection bias. This represents a missed opportunity to leverage the full utility of DAGs to depict associations between the variables we actually analyse in practice: empirically measured variables, which are necessarily measured with error. In this article, we focus on applying causal diagrams to depict the data-generating mechanisms that give rise to the data we analyse, including measurement error. We begin by considering empirical data considerations using a general example, and then build up to a specific worked example from the clinical epidemiology of hearing health. Throughout, our goal is to highlight both the challenges and the benefits of using DAGs to depict measurement error. In addition to the application of DAGs to conceptual causal questions (which pertain to unmeasured constructs free from measurement error), which is common, we highlight the advantages associated with applying DAGs to also include empirically measured variables and-potentially-information bias. We also highlight the implications implied by this use of DAGs, particularly regarding the unblocked backdoor path causal structure. Ultimately, we seek to help increase the clarity with which epidemiologists can map traditional epidemiological concepts (such as information bias and confounding) onto causal graphical structures., (© The Author(s) 2024; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)

With the ever advancing of modern technologies, it has become increasingly common that the number of collected confounders exceeds the number of subjects in a data set. However, matching based methods for estimating causal treatment effect in their original forms are not capable of handling high-dimensional confounders, and their various modified versions lack statistical support and valid inference tools. In this article, we propose a new approach for estimating causal treatment effect, defined as the difference of the restricted mean survival time (RMST) under different treatments in high-dimensional setting for survival data. We combine the factor model and the sufficient dimension reduction techniques to construct propensity score and prognostic score. Based on these scores, we develop a kernel based doubly robust estimator of the RMST difference. We demonstrate its link to matching and establish the consistency and asymptotic normality of the estimator. We illustrate our method by analyzing a dataset from a study aimed at comparing the effects of two alternative treatments on the RMST of patients with diffuse large B cell lymphoma., (© The Author(s) 2024. Published by Oxford University Press on behalf of The International Biometric Society.)

Establishing cause-effect relationships from observational data often relies on untestable assumptions. It is crucial to know whether, and to what extent, the conclusions drawn from non-experimental studies are robust to potential unmeasured confounding. In this paper, we focus on the average causal effect (ACE) as our target of inference. We generalize the sensitivity analysis approach developed by Robins et al., Franks et al., and Zhou and Yao. We use semiparametric theory to derive the non-parametric efficient influence function of the ACE, for fixed sensitivity parameters. We use this influence function to construct a one-step, split sample, truncated estimator of the ACE. Our estimator depends on semiparametric models for the distribution of the observed data; importantly, these models do not impose any restrictions on the values of sensitivity analysis parameters. We establish sufficient conditions ensuring that our estimator has $\sqrt{n}$ asymptotics. We use our methodology to evaluate the causal effect of smoking during pregnancy on birth weight. We also evaluate the performance of estimation procedure in a simulation study., (© The Author(s) 2024. Published by Oxford University Press on behalf of The International Biometric Society.)

Pharmacoepidemiological studies provide important information on the safety and effectiveness of medications, but the validity of study findings can be threatened by residual bias. Ideally, biases would be minimized through appropriate study design and statistical analysis methods. However, residual biases can remain, for example, due to unmeasured confounders, measurement error, or selection into the study. A group of sensitivity analysis methods, termed quantitative bias analyses, are available to assess, quantitatively and transparently, the robustness of study results to these residual biases. These approaches include methods to quantify how the estimated effect would be altered under specified assumptions about the potential bias, and methods to calculate bounds on effect estimates. This article introduces quantitative bias analyses for unmeasured confounding, misclassification, and selection bias, with a focus on their relevance and application to pharmacoepidemiological studies., (© 2024 The Author(s). Pharmacoepidemiology and Drug Safety published by John Wiley & Sons Ltd.)

Competing Interests: We declare no competing interests.

Aim: Non-randomized studies on bariatric surgery have reported large reductions in mortality within 6-12 months after surgery compared with non-surgical patients. It is unclear whether these findings are the result of bias., Study Design and Setting: We searched PubMed to identify all non-randomized studies investigating the effect of bariatric surgery on all-cause mortality compared with non-surgical patients. We assessed these studies for potential confounding and time-related biases. We conducted bias analyses to quantify the effect of these biases., Results: We identified 21 cohort studies that met our inclusion criteria. Among those, 11 were affected by immortal time bias resulting from the misclassification or exclusion of relevant follow-up time. Five studies were subject to potential confounding bias because of a lack of adjustment for body mass index (BMI). All studies used an inadequate comparator group that lacked indications for bariatric surgery. Bias analyses to correct for potential confounding from BMI shifted the effect estimates towards the null [reported hazard ratio (HR): 0.78 vs. bias-adjusted HR: 0.92]. Bias analyses to correct for the presence of immortal time also shifted the effect estimates towards the null (adjustment for 2-year wait time: reported HR: 0.57 vs. bias-adjusted HR: 0.81)., Conclusion: Several important sources of bias were identified in non-randomized studies of the effectiveness of bariatric surgery versus non-surgical comparators on mortality. Future studies should ensure that confounding by BMI is accounted for, considering the choice of the comparator group, and that the design or analysis avoids immortal time bias from the misclassification or exclusion., (© 2024 John Wiley & Sons Ltd.)

Objective: We aimed to demonstrate the use of quantitative bias analysis (QBA), which reveals the effects of systematic error, including confounding, misclassification and selection bias, on study results in epidemiological studies published in the period from 2010 to mid-23., Method: The articles identified through a keyword search using Pubmed and Scopus were included in the study. The articles obtained from this search were eliminated according to the exclusion criteria, and the articles in which QBA analysis was applied were included in the detailed evaluation., Results: It can be said that the application of QBA analysis has gradually increased over the 13-year period. Accordingly, the number of articles in which simple is used as a method in QBA analysis is 9 (9.89%), the number of articles in which the multidimensional approach is used is 10 (10.99%), the number of articles in which the probabilistic approach is used is 60 (65.93%) and the number of articles in which the method is not specified is 12 (13.19%). The number of articles with misclassification bias model is 44 (48.35%), the number of articles with uncontrolled confounder(s) bias model is 32 (35.16%), the number of articles with selection bias model is 7 (7.69%) and the number of articles using more than one bias model is 8 (8.79%). Of the 49 (53.85%) articles in which the bias parameter source was specified, 19 (38.78%) used internal validation, 26 (53.06%) used external validation and 4 (8.16%) used educated guess, data constraints and hypothetical data. Probabilistic approach was used as a bias method in 60 (65.93%) of the articles, and mostly beta (8 [13.33%)], normal (9 [15.00%]) and uniform (8 [13.33%]) distributions were selected., Conclusion: The application of QBA is rare in the literature but is increasing over time. Future researchers should include detailed analyzes such as QBA analysis to obtain inferences with higher evidence value, taking into account systematic errors., (© 2024 John Wiley & Sons Ltd.)

Valid inference of cause-and-effect relations in observational studies necessitates adjusting for common causes of the focal predictor (i.e., treatment) and the outcome. When such common causes, henceforth termed confounders, remain unadjusted for, they generate spurious correlations that lead to biased causal effect estimates. But routine adjustment for all available covariates, when only a subset are truly confounders, is known to yield potentially inefficient and unstable estimators. In this article, we introduce a data-driven confounder selection strategy that focuses on stable estimation of the treatment effect. The approach exploits the causal knowledge that after adjusting for confounders to eliminate all confounding biases, adding any remaining non-confounding covariates associated with only treatment or outcome, but not both, should not systematically change the effect estimator. The strategy proceeds in two steps. First, we prioritize covariates for adjustment by probing how strongly each covariate is associated with treatment and outcome. Next, we gauge the stability of the effect estimator by evaluating its trajectory adjusting for different covariate subsets. The smallest subset that yields a stable effect estimate is then selected. Thus, the strategy offers direct insight into the (in)sensitivity of the effect estimator to the chosen covariates for adjustment. The ability to correctly select confounders and yield valid causal inferences following data-driven covariate selection is evaluated empirically using extensive simulation studies. Furthermore, we compare the introduced method empirically with routine variable selection methods. Finally, we demonstrate the procedure using two publicly available real-world datasets. A step-by-step practical guide with user-friendly R functions is included. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The inability to correctly account for unmeasured confounding can lead to bias in parameter estimates, invalid uncertainty assessments, and erroneous conclusions. Sensitivity analysis is an approach to investigate the impact of unmeasured confounding in observational studies. However, the adoption of this approach has been slow given the lack of accessible software. An extensive review of available R packages to account for unmeasured confounding list deterministic sensitivity analysis methods, but no R packages were listed for probabilistic sensitivity analysis. The R package unmconf implements the first available package for probabilistic sensitivity analysis through a Bayesian unmeasured confounding model. The package allows for normal, binary, Poisson, or gamma responses, accounting for one or two unmeasured confounders from the normal or binomial distribution. The goal of unmconf is to implement a user friendly package that performs Bayesian modeling in the presence of unmeasured confounders, with simple commands on the front end while performing more intensive computation on the back end. We investigate the applicability of this package through novel simulation studies. The results indicate that credible intervals will have near nominal coverage probability and smaller bias when modeling the unmeasured confounder(s) for varying levels of internal/external validation data across various combinations of response-unmeasured confounder distributional families., (© 2024. The Author(s).)

Background: Comparing causal effect estimates obtained using observational data to those obtained from the gold standard (i.e., randomized controlled trials [RCTs]) helps assess the validity of these estimates. However, comparisons are challenging due to differences between observational data and RCT generated data. The unknown treatment assignment mechanism in the observational data and varying sampling mechanisms between the RCT and the observational data can lead to confounding and sampling bias, respectively., Aims: The objective of this study is to propose a two-step framework to validate causal effect estimates obtained from observational data by adjusting for both mechanisms., Materials and Methods: An estimator of causal effects related to the two mechanisms is constructed. A two-step framework for comparing causal effect estimates is derived from the estimator. An R package RCTrep is developed to implement the framework in practice., Results: A simulation study is conducted to show that using our framework observational data can produce causal effect estimates similar to those of an RCT. A real-world application of the framework to validate treatment effects of adjuvant chemotherapy obtained from registry data is demonstrated., Conclusion: This  study constructs a framework for comparing causal effect estimates between observational data and RCT data, facilitating the assessment of the validity of causal effect estimates obtained from observational data., (© 2024 The Author(s). Pharmacoepidemiology and Drug Safety published by John Wiley & Sons Ltd.)

Introduction: Comparative studies on surgical treatments with time-to-event endpoints have provided substantial evidence for clinical practice, but the accurate use of survival data analysis and the control of confounding bias remain big challenges., Methods: This was a survey of surgical studies with survival outcomes published in four general medical journals and five general surgical journals in 2021. The two most concerned statistical issues were evaluated, including confounding control by propensity score analysis (PSA) or multivariable analysis and testing of proportional hazards (PH) assumption in Cox model., Results: A total of 74 studies were included, comprising 63 observational studies and 11 randomized controlled trials. Among the observational studies, the proportion of studies utilizing PSA in surgical oncology and non-oncology studies was similar (40.9 % versus 36.8 %, P = 0.762). However, the former reported a significantly lower proportion of PH assumption assessments compared to the latter (13.6 % versus 42.1 %, P = 0.020). Twenty-five observational studies (25/63) used PSA methods, but two-thirds of them (17/25) showed unclear balance of baseline data after PSA. And the proportion of PH assumption testing after PSA was slightly lower than that before PSA, but the difference was not statistically significant (24.0 % versus 28.0 %, P = 0.317). Comprehensive suggestions were given on confounding control in survival analysis and alternative resolutions for non-compliance with PH assumption., Conclusion: This study highlights suboptimal reporting of PH assumption evaluation in observational surgical studies both before and after PSA. Efforts and consensus are needed with respect to the underlying assumptions of statistical methods., Competing Interests: Declaration of Competing Interest None., (Copyright © 2024 Elsevier Ltd, BASO ~ The Association for Cancer Surgery, and the European Society of Surgical Oncology. All rights reserved.)

Investigators often believe that relative effect measures conditional on covariates, such as risk ratios and mean ratios, are "transportable" across populations. Here, we examine the identification of causal effects in a target population using an assumption that conditional relative effect measures are transportable from a trial to the target population. We show that transportability for relative effect measures is largely incompatible with transportability for difference effect measures, unless the treatment has no effect on average or one is willing to make even stronger transportability assumptions that imply the transportability of both relative and difference effect measures. We then describe how marginal (population-averaged) causal estimands in a target population can be identified under the assumption of transportability of relative effect measures, when we are interested in the effectiveness of a new experimental treatment in a target population where the only treatment in use is the control treatment evaluated in the trial. We extend these results to consider cases where the control treatment evaluated in the trial is only one of the treatments in use in the target population, under an additional partial exchangeability assumption in the target population (i.e., an assumption of no unmeasured confounding in the target population with respect to potential outcomes under the control treatment in the trial). We also develop identification results that allow for the covariates needed for transportability of relative effect measures to be only a small subset of the covariates needed to control confounding in the target population. Last, we propose estimators that can be easily implemented in standard statistical software and illustrate their use using data from a comprehensive cohort study of stable ischemic heart disease., (© 2024. Springer Nature B.V.)

Background: Ultra-processed food (UPF) consumption is associated prospectively with weight gain and obesity in observational studies of adults. Unaccounted for confounding is a risk when attempting to make causal inference from observational studies. Limited research has examined how feasible it is that unmeasured confounding may explain associations between UPF consumption and weight gain in observational research., Methods: We introduce the E value to obesity researchers. The E value is defined as the minimum hypothetical strength of association that one or more unaccounted for confounding variables would need to have with an exposure (UPF consumption) and outcome (weight gain) to explain the association between the exposure and outcome of interest. We meta-analysed prospective studies on the association between UPF consumption and weight gain in adults to provide an effect estimation. Next, we applied the E value approach to this effect estimate and illustrated the potential role that unmeasured or hypothetical residual confounding variables could theoretically have in explaining associations., Results: Higher consumption of UPFs was associated with increased weight gain in meta-analysis (RR = 1.14). The corresponding E value = 1.55, indicating that unaccounted for confounding variables with small-to-moderate sized associations with UPF consumption and weight gain (e.g., depressive symptoms, trait overeating tendencies, access to healthy and nutritious food) could individually or collectively hypothetically account for observed associations between UPF consumption and weight gain., Conclusions: Unaccounted for confounding could plausibly explain the prospective association between UPF consumption and weight gain in adults. High quality observational research controlling for potential confounders and evidence from study types devoid of confounding are now needed., (© 2024. The Author(s).)

Competing Interests: The author reports no conflicts of interest.

Background: High-dimensional propensity scoring (HDPS) is a method for empirically identifying potential confounders within large healthcare databases such as administrative claims data. However, this method has not yet been applied to large national health surveys such as the National Health and Aging Trends Study (NHATS), an ongoing nationally representative survey of older adults in the United States and important resource in gerontology research., Methods: In this Research Practice article, we present an overview of HDPS and describe the specific data transformation steps and analytic considerations needed to apply it to national health surveys. We applied HDPS within NHATS to investigate the association between self-reported visual difficulty and incident dementia, comparing HDPS to conventional confounder selection methods., Results: Among 7 207 dementia-free NHATS Wave 1 respondents, 528 (7.3%) had self-reported visual difficulty. In an unadjusted discrete time proportional hazards model accounting for the complex survey design of NHATS, self-reported visual difficulty was strongly associated with incident dementia (odds ratio [OR] 2.34, 95% confidence interval [CI]: 1.95-2.81). After adjustment for standard investigator-selected covariates via inverse probability weighting, the magnitude of this association decreased, but evidence of an association remained (OR 1.44, 95% CI: 1.11-1.85). Adding 75 HDPS-prioritized variables to the investigator-selected propensity score model resulted in further attenuation of the association between visual impairment and dementia (OR 0.94, 95% CI: 0.70-1.23)., Conclusions: HDPS can be successfully applied to national health surveys such as NHATS and may improve confounder adjustment. We hope developing this framework will encourage future consideration of HDPS in this setting., (© The Author(s) 2024. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Background: Spontaneous Adverse Event Reporting (SAER) databases play a crucial role in post-marketing drug surveillance. However, the traditional model-free disproportionality analysis has been challenged by the insufficiency in investigating subgroup and confounders. These issues result in significant low-precision and biases in data mining for SAER., Methods: The Model-Driven Reporting Odds Ratio (MD-ROR) was proposed to bridge the gap between SAER database and explainable models for exploring individual and confounding effects. MD-ROR is grounded in a well-designed model, rather than a 2 × 2 cross table, for estimating AE-drug signals. Consequently, individual and confounding effects can be parameterized based on these models. We employed simulation data and the FDA Adverse Event Reporting System (FAERS) database., Result: The simulated data indicated the subgroup effects estimated by MD-ROR were unbiased and efficient. Moreover, the adjusted-MD-ROR demonstrated greater robustness against confounding biases than the crude ROR. Applying our method to the FAERS database suggested higher occurrences of drug interactions and cardiac adverse events induced by Midazolam in females compared to males., Conclusion: The study underscored that MD-ROR holds promise as a method for investigating individual and confounding effects in SAER databases.

Background: Disease latency is defined as the time from disease initiation to disease diagnosis. Disease latency bias (DLB) can arise in epidemiological studies that examine latent outcomes, since the exact timing of the disease inception is unknown and might occur before exposure initiation, potentially leading to bias. Although DLB can affect epidemiological studies that examine different types of chronic disease (e.g. Alzheimer's disease, cancer etc), the manner by which DLB can introduce bias into these studies has not been previously elucidated. Information on the specific types of bias, and their structure, that can arise secondary to DLB is critical for researchers, to enable better understanding and control for DLB., Development: Here we describe four scenarios by which DLB can introduce bias (through different structures) into epidemiological studies that address latent outcomes, using directed acyclic graphs (DAGs). We also discuss potential strategies to better understand, examine and control for DLB in these studies., Application: Using causal diagrams, we show that disease latency bias can affect results of epidemiological studies through: (i) unmeasured confounding; (ii) reverse causality; (iii) selection bias; (iv) bias through a mediator., Conclusion: Disease latency bias is an important bias that can affect a number of epidemiological studies that address latent outcomes. Causal diagrams can assist researchers better identify and control for this bias., (© The Author(s) 2024; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Observational studies are frequently used in clinical research to estimate the effects of treatments or exposures on outcomes. To reduce the effects of confounding when estimating treatment effects, covariate balancing methods are frequently implemented. This study evaluated, using extensive Monte Carlo simulation, several methods of covariate balancing, and two methods for propensity score estimation, for estimating the average treatment effect on the treated using a hazard ratio from a Cox proportional hazards model. With respect to minimizing bias and maximizing accuracy (as measured by the mean square error) of the treatment effect, the average treatment effect on the treated weighting, fine stratification, and optimal full matching with a conventional logistic regression model for the propensity score performed best across all simulated conditions. Other methods performed well in specific circumstances, such as pair matching when sample sizes were large (n = 5000) and the proportion treated was < 0.25. Statistical power was generally higher for weighting methods than matching methods, and Type I error rates were at or below the nominal level for balancing methods with unbiased treatment effect estimates. There was also a decreasing effective sample size with an increasing number of strata, therefore for stratification-based weighting methods, it may be important to consider fewer strata. Generally, we recommend methods that performed well in our simulations, although the identification of methods that performed well is necessarily limited by the specific features of our simulation. The methods are illustrated using a real-world example comparing beta blockers and angiotensin-converting enzyme inhibitors among hypertensive patients at risk for incident stroke., Competing Interests: Declaration of conflicting interestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Guy Cafri and Stephen Fortin are employees of Johnson & Johnson.

Propensity score methods are popular to control for confounding in observational biomedical studies of risk factors or medical treatments. This paper focused on aspects of propensity score methods that often remain undiscussed, including unmeasured confounding, missing data, variable selection, statistical efficiency, estimands, the positivity assumption, and predictive performance of the propensity score model., Competing Interests: Conflict of interest: R.H.H.G. and S.l.C. report no conflicts of interest. O.M.D. is a deputy editor for European Journal of Endocrinology. He was not involved in the review or editorial process for this paper, on which he is listed as an author., (© The Author(s) 2024. Published by Oxford University Press on behalf of European Society of Endocrinology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

We propose a new Bayesian nonparametric method for estimating the causal effects of mediation in the presence of a post-treatment confounder. The methodology is motivated by the Rural Lifestyle Intervention Treatment Effectiveness Trial (Rural LITE) for which there is interest in estimating causal mediation effects but is complicated by the presence of a post-treatment confounder. We specify an enriched Dirichlet process mixture (EDPM) to model the joint distribution of the observed data (outcome, mediator, post-treatment confounder, treatment, and baseline confounders). For identifiability, we use the extended version of the standard sequential ignorability (SI) as introduced in Hong et al. along with a Gaussian copula model assumption. The observed data model and causal identification assumptions enable us to estimate and identify the causal effects of mediation, that is, the natural direct effects (NDE) and natural indirect effects (NIE). Our method enables easy computation of NIE and NDE for a subset of confounding variables and addresses missing data through data augmentation under the assumption of ignorable missingness. We conduct simulation studies to assess the performance of our proposed method. Furthermore, we apply this approach to evaluate the causal mediation effect in the Rural LITE trial, finding that there was not strong evidence for the potential mediator., (© The Author(s) 2024. Published by Oxford University Press on behalf of The International Biometric Society.)

Sporadic fundic gland polyps (FGPs) progress, albeit rarely, to dysplasia and cancer. Two meta-analyses, including 8 and 11 studies, concluded that proton pump inhibitors (PPIs) were associated with FGPs. Intervention is considered unnecessary when FGPs have a background of PPIs use. Both meta-analyses, however, disregarded known confounders: age, sex, endoscopy indications, study design (prospective or retrospective), duration of PPI use, and H. pylori infection. Confounders are known to invalidate meta-analyses. We followed PRIXMA guidelines and searched the literature for studies on FGPs in PPI-users and PPI-nonusers. In the 22 studies searched, we compared FGPs in PPI-users (n = 6534) and PPI-nonusers (n = 41 115). Heterogeneity was significant (Cochran Q = 277.8, P < 0.0001; I2 = 92.8%), annulling meta-analysis performed by blanket tallying. To offset the above confounders, we matched PPI-users and PPI-nonusers by (a) age and sex (n = 4300 and 29 307, respectively) and (b) their propensity scores derived from the confounders (n = 2950 and 4729, respectively). After both matching, FGPs were not significantly different between PPI-users and PPI-nonusers [odds ratio (OR) = 1.1, P = 0.3078; OR = 0.9, P = 0.3258, respectively]. Furthermore, FGP frequency did not correlate with increasing duration of PPI use (Pearson and Spearman correlation coefficients = 0.1162, 0.0386, P < 0.6064, 0.8646, respectively); it was not significantly different between any of the duration periods of observation, namely, <10, 10-20, 20-40, >40 months, nor was it significantly different between PPI-users and PPI-nonusers within each duration period (P > 0.05). We conclude that PPIs are not associated with FGPs, implying that a background history of PPI use is not a justification for nonintervention in the management of FGPs., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)

Urban environments, characterized by bustling mass transit systems and high population density, host a complex web of microorganisms that impact microbial interactions. These urban microbiomes, influenced by diverse demographics and constant human movement, are vital for understanding microbial dynamics. We explore urban metagenomics, utilizing an extensive dataset from the Metagenomics & Metadesign of Subways & Urban Biomes (MetaSUB) consortium, and investigate antimicrobial resistance (AMR) patterns. In this pioneering research, we delve into the role of bacteriophages, or "phages"-viruses that prey on bacteria and can facilitate the exchange of antibiotic resistance genes (ARGs) through mechanisms like horizontal gene transfer (HGT). Despite their potential significance, existing literature lacks a consensus on their significance in ARG dissemination. We argue that they are an important consideration. We uncover that environmental variables, such as those on climate, demographics, and landscape, can obscure phage-resistome relationships. We adjust for these potential confounders and clarify these relationships across specific and overall antibiotic classes with precision, identifying several key phages. Leveraging machine learning tools and validating findings through clinical literature, we uncover novel associations, adding valuable insights to our comprehension of AMR development., (© 2024 John Wiley & Sons Ltd.)

Background: Cardiovascular disease (CVD) is the leading cause of death worldwide. It has been known for some considerable time that radiation is associated with excess risk of CVD. A recent systematic review of radiation and CVD highlighted substantial inter-study heterogeneity in effect, possibly a result of confounding or modifications of radiation effect by non-radiation factors, in particular by the major lifestyle/environmental/medical risk factors and latent period., Methods: We assessed effects of confounding by lifestyle/environmental/medical risk factors on radiation-associated CVD and investigated evidence for modifying effects of these variables on CVD radiation dose-response, using data assembled for a recent systematic review., Results: There are 43 epidemiologic studies which are informative on effects of adjustment for confounding or risk modifying factors on radiation-associated CVD. Of these 22 were studies of groups exposed to substantial doses of medical radiation for therapy or diagnosis. The remaining 21 studies were of groups exposed at much lower levels of dose and/or dose rate. Only four studies suggest substantial effects of adjustment for lifestyle/environmental/medical risk factors on radiation risk of CVD; however, there were also substantial uncertainties in the estimates in all of these studies. There are fewer suggestions of effects that modify the radiation dose response; only two studies, both at lower levels of dose, report the most serious level of modifying effect., Conclusions: There are still large uncertainties about confounding factors or lifestyle/environmental/medical variables that may influence radiation-associated CVD, although indications are that there are not many studies in which there are substantial confounding effects of these risk factors., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Background: Perceived discrimination in health care settings can have adverse consequences on mental health in minority groups. However, the association between perceived discrimination and mental health is prone to unmeasured confounding. The study aims to quantitatively evaluate the influence of unmeasured confounding in this association, using g-estimation., Methods: In a predominantly African American cohort, we applied g-estimation to estimate the association between perceived discrimination and mental health, adjusted and unadjusted for measured confounders. Mental health was measured using clinical diagnoses of anxiety, depression and bipolar disorder. Perceived discrimination was measured as the number of patient-reported discrimination events in health care settings. Measured confounders included demographic, socioeconomic, residential and health characteristics. The influence of confounding was denoted as α1 from g-estimation. We compared α1 for measured and unmeasured confounding., Results: Strong associations between perceived discrimination in health care settings and mental health outcomes were observed. For anxiety, the odds ratio (95% confidence interval) unadjusted and adjusted for measured confounders were 1.30 (1.21, 1.39) and 1.26 (1.17, 1.36), respectively. The α1 for measured confounding was -0.066. Unmeasured confounding with α1=0.200, which was over three times that of measured confounding, corresponds to an odds ratio of 1.12 (1.01, 1.24). Similar results were observed for other mental health outcomes., Conclusion: Compared with measured confounding, unmeasured that was three times measured confounding was not enough to explain away the association between perceived discrimination and mental health, suggesting that this association is robust to unmeasured confounding. This study provides a novel framework to quantitatively evaluate unmeasured confounding., (© The Author(s) 2024; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Background: Observational studies are frequently used to estimate the comparative effectiveness of different colorectal cancer (CRC) screening methods due to the practical limitations and time needed to conduct large clinical trials. However, time-varying confounders, e.g. polyp detection in the last screening, can bias statistical results. Recently, generalized methods, or G-methods, have been used for the analysis of observational studies of CRC screening, given their ability to account for such time-varying confounders. Discretization, or the process of converting continuous functions into discrete counterparts, is required for G-methods when the treatment and outcomes are assessed at a continuous scale., Development: This paper evaluates the interplay between time-varying confounding and discretization, which can induce bias in assessing screening effectiveness. We investigate this bias in evaluating the effect of different CRC screening methods that differ from each other in typical screening frequency., Application: First, using theory, we establish the direction of the bias. Then, we use simulations of hypothetical settings to study the bias magnitude for varying levels of discretization, frequency of screening and length of the study period. We develop a method to assess possible bias due to coarsening in simulated situations., Conclusions: The proposed method can inform future studies of screening effectiveness, especially for CRC, by determining the choice of interval lengths where data are discretized to minimize bias due to coarsening while balancing computational costs., (© The Author(s) 2024; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Background: Mediation analysis is a powerful tool to identify factors mediating the causal pathway of exposure to health outcomes. Mediation analysis has been extended to study a large number of potential mediators in high-dimensional data settings. The presence of confounding in observational studies is inevitable. Hence, it's an essential part of high-dimensional mediation analysis (HDMA) to adjust for the potential confounders. Although the propensity score (PS) related method such as propensity score regression adjustment (PSR) and inverse probability weighting (IPW) has been proposed to tackle this problem, the characteristics with extreme propensity score distribution of the PS-based method would result in the biased estimation., Methods: In this article, we integrated the overlapping weighting (OW) technique into HDMA workflow and proposed a concise and powerful high-dimensional mediation analysis procedure consisting of OW confounding adjustment, sure independence screening (SIS), de-biased Lasso penalization, and joint-significance testing underlying the mixture null distribution. We compared the proposed method with the existing method consisting of PS-based confounding adjustment, SIS, minimax concave penalty (MCP) variable selection, and classical joint-significance testing., Results: Simulation studies demonstrate the proposed procedure has the best performance in mediator selection and estimation. The proposed procedure yielded the highest true positive rate, acceptable false discovery proportion level, and lower mean square error. In the empirical study based on the GSE117859 dataset in the Gene Expression Omnibus database using the proposed method, we found that smoking history may lead to the estimated natural killer (NK) cell level reduction through the mediation effect of some methylation markers, mainly including methylation sites cg13917614 in CNP gene and cg16893868 in LILRA2 gene., Conclusions: The proposed method has higher power, sufficient false discovery rate control, and precise mediation effect estimation. Meanwhile, it is feasible to be implemented with the presence of confounders. Hence, our method is worth considering in HDMA studies., (© 2024. The Author(s).)

Purpose: In occupational epidemiology, the healthy worker survivor effect can manifest as a time-dependent confounder because healthier workers can accrue greater amounts of exposure over longer periods of employment. For example, in occupational studies of radiation exposure that focus on cumulative annualized radiation dose, workers can accrue greater amounts of cumulative radiation exposure over longer periods of employment, while workers with longer periods of employment can transition into jobs with a reduced potential for annualized radiation exposure. The extent to which confounding arising from the healthy worker survivor effect impacts radiation risk estimates is unknown., Methods: We assessed the impact of the healthy worker survivor effect on estimates of radiation risk among nuclear workers in a Million Person Study cohort. In simulation studies, we contrasted the ability of marginal structural Cox models with inverse probability weighting and Cox proportional hazards models to account for time-dependent confounding arising from the healthy worker survivor effect., Results: Marginal structural Cox models and Cox proportional hazards models with flexible functional forms for duration of employment provided reliable results., Conclusions: It is crucial to flexibly adjust for duration of employment to account for confounding arising from the healthy worker survivor effect in occupational epidemiology., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Objectives: Causal inference methods for observational data represent an alternative to randomised controlled trials when they are not feasible or when real-world evidence is sought. Inverse-probability-of-treatment weighting (IPTW) is one of the most popular approaches to account for confounding in observational studies. In medical research, IPTW is mainly applied to estimate the causal effect of a binary treatment, even when the treatment has in fact multiple categories, despite the availability of IPTW estimators for multiple treatment categories. This raises questions about the appropriateness of the use of IPTW in this context. Therefore, we conducted a systematic review of medical publications reporting the use of IPTW in the presence of a multi-category treatment. Our objectives were to investigate the frequency of use and the implementation of these methods in practice, and to assess the quality of their reporting., Study Design and Setting: Using Pubmed, Embase and Web of Science, we screened 5660 articles and retained 106 articles in the final analysis that were from 17 different medical areas. This systematic review is registered on PROSPERO (CRD42022352669)., Results: The number of treatment groups varied between 3 and 9, with a large majority of articles (90 [84.9%]) including 3 or 4 groups. The most commonly used method for estimating the weights was multinomial regression (51 [48.1%]) and generalized boosted models (48 [45.3%]). The covariates of the weight model were reported in 91 articles (85.9 %). Twenty-six articles (24.5 %) did not discuss the balance of covariates after weighting, and only 16 articles (15.1 %) referred to the assumptions needed to obtain correct inferences., Conclusion: The results of this systematic review illustrate that medical publications scarcely use IPTW methods for more than two treatment categories. Among the publications that did, the quality of reporting was suboptimal, in particular in regard to the assumptions and model building. IPTW for multi-category treatments could be applied more broadly in medical research, and the application of the proposed guidelines in this context will help researchers to report their results and to ensure reproducibility of their research., Competing Interests: Declaration of competing interest None., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Obesity and internalising disorders, including depression and anxiety, often co-occur. There is evidence that familial confounding contributes to the co-occurrence of internalising disorders and obesity in adults. However, its impact on this association among young people is unclear. Our study investigated the extent to which familial factors confound the association between internalising disorders and obesity in adolescents and young adults., Subjects/methods: We used a matched co-twin design to investigate the impact of confounding by familial factors on associations between internalising symptoms and obesity in a sample of 4018 twins aged 16 to 27 years., Results: High levels of internalising symptoms compared to low levels increased the odds of obesity for the whole cohort (adjusted odds ratio [AOR] = 3.1, 95% confidence interval [CI]: 1.5, 6.8), and in females (AOR = 4.1, 95% CI 1.5, 11.1), but not in males (AOR = 2.8 95% CI 0.8, 10.0). We found evidence that internalising symptoms were associated with an increased between-pair odds of obesity (AOR 6.2, 95% CI 1.7, 22.8), using the paired analysis but not using a within-pair association, which controls for familial confounding. Sex-stratified analyses indicated high internalising symptoms were associated with increased between-pair odds of obesity for females (AOR 12.9, 95% CI 2.2, 76.8), but this attenuated to the null using within-pair analysis. We found no evidence of between or within-pair associations for males and weak evidence that sex modified the association between internalising symptoms and obesity (likelihood ratio test p = 0.051)., Conclusions: Some familial factors shared by twins confound the association between internalising symptoms and obesity in adolescent and young adult females. Internalising symptoms and obesity were not associated for adolescent and young adult males. Therefore, prevention and treatment efforts should especially address familial shared determinants of obesity, particularly targeted at female adolescents and young adults with internalising symptoms and those with a family history of these disorders., (© 2024. The Author(s).)

Epidemiological researchers often examine associations between risk factors and health outcomes in non-experimental designs. Observed associations may be causal or confounded by unmeasured factors. Sibling and co-twin control studies account for familial confounding by comparing exposure levels among siblings (or twins). If the exposure-outcome association is causal, the siblings should also differ regarding the outcome. However, such studies may sometimes introduce more bias than they alleviate. Measurement error in the exposure may bias results and lead to erroneous conclusions that truly causal exposure-outcome associations are confounded by familial factors. The current study used Monte Carlo simulations to examine bias due to measurement error in sibling control models when the observed exposure-outcome association is truly causal. The results showed that decreasing exposure reliability and increasing sibling-correlations in the exposure led to deflated exposure-outcome associations and inflated associations between the family mean of the exposure and the outcome. The risk of falsely concluding that causal associations were confounded was high in many situations. For example, when exposure reliability was 0.7 and the observed sibling-correlation was r = 0.4, about 30-90% of the samples (n = 2,000) provided results supporting a false conclusion of confounding, depending on how p-values were interpreted as evidence for a family effect on the outcome. The current results have practical importance for epidemiological researchers conducting or reviewing sibling and co-twin control studies and may improve our understanding of observed associations between risk factors and health outcomes. We have developed an app (SibSim) providing simulations of many situations not presented in this paper., (© 2024. The Author(s).)